## What
`model.safetensors` in this repo was missing the entire vision tower
(all 208 `vision_model.*` tensors):
- **full mirrors**: the file was byte-identical to `text_model.safetensors`
(200 tensors: `text_model.*` + `logit_scale`/`logit_bias`; 1.13 GB) — the
text-only blob had been uploaded under both names. The complete upstream
checkpoint is 1.50 GB / 408 tensors.
- **vision splits**: the file contained only `logit_scale`/`logit_bias`
(144 bytes).
## Impact
Anything loading the repo gets a **silently random-initialized vision tower**
(`from_pretrained` warns about missing keys and continues). Concretely: the
aiWARE `siglip2-chunk` engine bakes this checkpoint into its Docker image at
build time; every image/video embedding it has produced is deterministic but
meaningless noise. Verified empirically: engine-stored vectors vs true SigLIP2
embeddings of the exact same video frames give cosine ~0.06 (receipts:
`services/playground/notebooks/siglip2_vector_verification.ipy…
